a project by joachimwerner
My goal is build on Alberto's work on "yomi" and the new Salt-based virtualization management features that Cedric has contributed, then combine them with a Redfish prototype to do the following from one (ideally idempotent) Salt state (orchestration state if required):
- mount the installation media via Redfish
- power-cycle the machine via Redfish as needed
- use yomi to install the virtualization host
- use yomi and Salt virtualization states to deploy a bunch of VMs that will be our Kubernetes nodes
- use yomi and/or skuba to create a Kubernetes cluster
The goal isn't to get this done perfectly well, but to learn about the potential challenges, especially with the end-to-end integration into Salt.
Looking for hackers with the skills:
This project is part of:
Hack Week 18
Activity
Comments
-
over 6 years ago by joachimwerner | Reply
First day results aren't that great. While I can communicate fine with my Redfish test system, I've learned that hard way that the one thing Redfish can't do well out of the box is exactly what I was planning to do: Mount a remote ISO to boot from it. There are vendor-specific extensions in some of the very latest Redfish implementations, but this really sucks a bit. :-(
Similar Projects
Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil
Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!
Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.
For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.
No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)
The idea is testing Salt (including bootstrapping with bootstrap script) and Salt-ssh clients
To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):
- Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
- Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
- Package management (install, remove, update...)
- Patching
- Applying any basic salt state (including a formula)
- Salt remote commands
- Bonus point: Java part for product identification, and monitoring enablement
- Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
- Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
- Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)
If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)
- If you don't have knowledge about some of the steps: ask the team
- If you still don't know what to do: switch to another distribution and keep testing.
This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)
In progress/done for Hack Week 25
Guide
We started writin a Guide: Adding a new client GNU Linux distribution to Uyuni at https://github.com/uyuni-project/uyuni/wiki/Guide:-Adding-a-new-client-GNU-Linux-distribution-to-Uyuni, to make things easier for everyone, specially those not too familiar wht Uyuni or not technical.
openSUSE Leap 16.0
The distribution will all love!
https://en.opensuse.org/openSUSE:Roadmap#DRAFTScheduleforLeap16.0
Curent Status We started last year, it's complete now for Hack Week 25! :-D
[W]Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file) NOTE: Done, client tools for SLMicro6 are using as those for SLE16.0/openSUSE Leap 16.0 are not available yet[W]Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)[W]Package management (install, remove, update...). Works, even reboot requirement detection
Ansible to Salt integration by vizhestkov
Description
We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.
In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.
It could be a good solution for the end users to reuse Ansible playbooks or run Ansible modules they got used to with no effort of complex configuration with existing Salt (or Uyuni/SUSE Multi Linux Manager) infrastructure.
Goals
- [v] Prepare the testing environment with Salt and Ansible installed
- [v] Discover Ansible codebase to figure out possible ways of integration
- [v] Create Salt/Uyuni inventory module
- [v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
- [v] Test some most basic playbooks
Resources
Enhance setup wizard for Uyuni by PSuarezHernandez
Description
This project wants to enhance the intial setup on Uyuni after its installation, so it's easier for a user to start using with it.
Uyuni currently uses "uyuni-tools" (mgradm) as the installation entrypoint, to trigger the installation of Uyuni in the given host, but does not really perform an initial setup, for instance:
- user creation
- adding products / channels
- generating bootstrap repos
- create activation keys
- ...
Goals
- Provide initial setup wizard as part of mgradm uyuni installation
Resources
Ansible to Salt integration by vizhestkov
Description
We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.
In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.
It could be a good solution for the end users to reuse Ansible playbooks or run Ansible modules they got used to with no effort of complex configuration with existing Salt (or Uyuni/SUSE Multi Linux Manager) infrastructure.
Goals
- [v] Prepare the testing environment with Salt and Ansible installed
- [v] Discover Ansible codebase to figure out possible ways of integration
- [v] Create Salt/Uyuni inventory module
- [v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
- [v] Test some most basic playbooks
Resources
Preparing KubeVirtBMC for project transfer to the KubeVirt organization by zchang
Description
KubeVirtBMC is preparing to transfer the project to the KubeVirt organization. One requirement is to enhance the modeling design's security. The current v1alpha1 API (the VirtualMachineBMC CRD) was designed during the proof-of-concept stage. It's immature and inherently insecure due to its cross-namespace object references, exposing security concerns from an RBAC perspective.
The other long-awaited feature is the ability to mount virtual media so that virtual machines can boot from remote ISO images.
Goals
- Deliver the v1beta1 API and its corresponding controller implementation
- Enable the Redfish virtual media mount function for KubeVirt virtual machines
Resources
- The KubeVirtBMC repo: https://github.com/starbops/kubevirtbmc
- The new v1beta1 API: https://github.com/starbops/kubevirtbmc/issues/83
- Redfish virtual media mount: https://github.com/starbops/kubevirtbmc/issues/44
Kubernetes-Based ML Lifecycle Automation by lmiranda
Description
This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.
The pipeline will automate the lifecycle of a machine learning model, including:
- Data ingestion/collection
- Model training as a Kubernetes Job
- Model artifact storage in an S3-compatible registry (e.g. Minio)
- A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
- A lightweight inference service that loads and serves the latest model
- Monitoring of model performance and service health through Prometheus/Grafana
The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.
Goals
By the end of Hack Week, the project should:
Produce a fully functional ML pipeline running on Kubernetes with:
- Data collection job
- Training job container
- Storage and versioning of trained models
- Automated deployment of new model versions
- Model inference API service
- Basic monitoring dashboards
Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.
Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).
Prepare a short demo explaining the end-to-end process and how new models flow through the system.
Resources
Updates
- Training pipeline and datasets
- Inference Service py
Rancher/k8s Trouble-Maker by tonyhansen
Project Description
When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.
Goals for Hackweek 25
- Update to modern Rancher and verify that existing tests still work
- Change testing logic to populate secrets instead of requiring a secondary script
- Add new tests
Goals for Hackweek 24 (Complete)
- Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix
- Create at least 5 modules that can be applied to the cluster and require troubleshooting
Resources
- https://github.com/celidon/rancher-troublemaker
- https://github.com/rancher/terraform-provider-rancher2
- https://github.com/rancher/tf-rancher-up
- https://github.com/rancher/quickstart
The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio
Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. 
The Plan
Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!
Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:
❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.
❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.
❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.
If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.
Why?
We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.
The CONCLUSION!!!
A
State of the Union
document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below!
Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0
Self-Scaling LLM Infrastructure Powered by Rancher

Description
The Problem
Running LLMs can get expensive and complex pretty quickly.
Today there are typically two choices:
- Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
- Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.
What if there was a middle ground?
What if infrastructure scaled itself instead of making you scale it?
Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?
Project Repository: github.com/alexander-demicev/llmserverless
What This Project Does
A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.
A complete, self-scaling LLM infrastructure that:
- Scales to zero when idle (no idle costs)
- Scales up automatically when requests come in
- Adds more nodes when needed, removes them when demand drops
- Runs on any infrastructure - laptop, bare metal, or cloud
Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.
How It Works
A combination of open source tools working together:
Flow:
- Users interact with OpenWebUI (chat interface)
- Requests go to LiteLLM Gateway
- LiteLLM routes requests to:
- Ollama (Knative) for local model inference (auto-scales pods)
- Or cloud APIs for fallback